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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports... -
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embedchain
Framework to easily create LLM powered bots over any dataset
Embedchain is a framework to easily create LLM-powered bots over any dataset. If you want a javascript version, check out embedchain-js. Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset. Start building LLM powered bots under 30 seconds. -
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LLMs-from-scratch
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
LLMs-from-scratch is an educational codebase that walks through implementing modern large-language-model components step by step. It emphasizes building blocks—tokenization, embeddings, attention, feed-forward layers, normalization, and training loops—so learners understand not just how to use a model but how it works internally. The repository favors clear Python and NumPy or PyTorch implementations that can be run and modified without heavyweight frameworks obscuring the logic. Chapters... -
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Phi-3-MLX
Phi-3.5 for Mac: Locally-run Vision and Language Models
Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips. -
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DB-GPT
Revolutionizing Database Interactions with Private LLM Technology
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can be assured that there is no risk of data leakage, and your data is 100% private and secure. -
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langrocks
Tools like web browser, computer access and code runner for LLMs
Langrocks is a programming language experimentation toolkit that enables developers to create, test, and optimize custom programming languages. -
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BISHENG
BISHENG is an open LLM devops platform for next generation apps
BISHENG is an open LLM application DevOps platform, focusing on enterprise scenarios. It has been used by a large number of industry-leading organizations and Fortune 500 companies. "Bi Sheng" was the inventor of movable type printing, which played a vital role in promoting the transmission of human knowledge. We hope that BISHENG can also provide strong support for the widespread implementation of intelligent applications. Everyone is welcome to participate. -
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Tongyi DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
DeepResearch (Tongyi DeepResearch) is an open-source “deep research agent” developed by Alibaba’s Tongyi Lab designed for long-horizon, information-seeking tasks. It’s built to act like a research agent: synthesizing, reasoning, retrieving information via the web and documents, and backing its outputs with evidence. The model is about 30.5 billion parameters in size, though at any given token only ~3.3B parameters are active. It uses a mix of synthetic data generation, fine-tuning and... -
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start... -
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LangChain
⚡ Building applications with LLMs through composability ⚡
Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. This library is aimed at assisting in the development of those types of applications. -
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GeneralAI
Large-scale Self-supervised Pre-training Across Tasks, Languages, etc.
Fundamental research to develop new architectures for foundation models and AI, focusing on modeling generality and capability, as well as training stability and efficiency. -
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Qwen-Image
Qwen-Image is a powerful image generation foundation model
Qwen-Image is a powerful 20-billion parameter foundation model designed for advanced image generation and precise editing, with a particular strength in complex text rendering across diverse languages, especially Chinese. Built on the MMDiT architecture, it achieves remarkable fidelity in integrating text seamlessly into images while preserving typographic details and layout coherence. The model excels not only in text rendering but also in a wide range of artistic styles, including... -
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LangChain-Chatchat
Langchain-Chatchat (formerly langchain-ChatGLM), local knowledge
LangChain-Chatchat (formerly Langchain-ChatGLM): A local knowledge base question answering application implementation based on large language models such as Langchain and ChatGLM. The knowledge base information of the current project is stored in the database, please initialize the database before running the project officially (we strongly recommend that you back up your knowledge files before performing operations). Relying on the open-source LLM and Embedding models supported by this... -
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ChatGLM2-6B
ChatGLM2-6B: An Open Bilingual Chat LLM
ChatGLM2-6B is the second-gen Chinese-English conversational LLM from ZhipuAI/Tsinghua. It upgrades the base model with GLM’s hybrid pretraining objective, 1.4 TB bilingual data, and preference alignment—delivering big gains on MMLU, CEval, GSM8K, and BBH. The context window extends up to 32K (FlashAttention), and Multi-Query Attention improves speed and memory use. The repo includes Python APIs, CLI & web demos, OpenAI-style/FASTAPI servers, and quantized checkpoints for lightweight local... -
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Scikit-LLM
Seamlessly integrate LLMs into scikit-learn
Seamlessly integrate powerful language models like ChatGPT into sci-kit-learn for enhanced text analysis tasks. At the moment the majority of the Scikit-LLM estimators are only compatible with some of the OpenAI models. Hence, a user-provided OpenAI API key is required. Additionally, Scikit-LLM will ensure that the obtained response contains a valid label. If this is not the case, a label will be selected randomly (label probabilities are proportional to label occurrences in the training... -
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Xorbits Inference
Replace OpenAI GPT with another LLM in your app
Replace OpenAI GPT with another LLM in your app by changing a single line of code. Xinference gives you the freedom to use any LLM you need. With Xinference, you're empowered to run inference with any open-source language models, speech recognition models, and multimodal models, whether in the cloud, on-premises, or even on your laptop. Xorbits Inference(Xinference) is a powerful and versatile library designed to serve language, speech recognition, and multimodal models. With Xorbits... -
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LLaMA 3
The official Meta Llama 3 GitHub site
This repository is the former home for Llama 3 model artifacts and getting-started code, covering pre-trained and instruction-tuned variants across multiple parameter sizes. It introduced the public packaging of weights, licenses, and quickstart examples that helped developers fine-tune or run the models locally and on common serving stacks. As the Llama stack evolved, Meta consolidated repositories and marked this one deprecated, pointing users to newer, centralized hubs for models,... -
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MiniMind
Train a 26M-parameter GPT from scratch in just 2h
minimind is a framework that enables users to train a 26-million-parameter GPT (Generative Pre-trained Transformer) model from scratch in approximately two hours. It provides a streamlined process for data preparation, model training, and evaluation, making it accessible for individuals and organizations to develop their own language models without extensive computational resources. -
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GPT Academic
Research-oriented chatbot framework
GPT Academic is a research-oriented chatbot framework designed to integrate large language models (LLMs) into academic workflows. It provides tools for structured document processing, citation management, and enhanced interaction with research papers. -
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MegaParse
File Parser optimised for LLM Ingestion with no loss
MegaParse is a file parser optimized for Large Language Model (LLM) ingestion, ensuring no loss of information. It efficiently parses various document formats, such as PDFs, DOCX, and PPTX, converting them into formats ideal for processing by LLMs. This tool is essential for applications that require accurate and comprehensive data extraction from diverse document types. -
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OpenLLMetry
Open-source observability for your LLM application
The repo contains standard OpenTelemetry instrumentations for LLM providers and Vector DBs, as well as a Traceloop SDK that makes it easy to get started with OpenLLMetry, while still outputting standard OpenTelemetry data that can be connected to your observability stack. If you already have OpenTelemetry instrumented, you can just add any of our instrumentations directly. -
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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ChatGLM3
ChatGLM3 series: Open Bilingual Chat LLMs | Open Source Bilingual Chat
ChatGLM3 is ZhipuAI & Tsinghua KEG’s third-gen conversational model suite centered on the 6B-parameter ChatGLM3-6B. It keeps the series’ smooth dialog and low deployment cost while adding native tool use (function calling), a built-in code interpreter, and agent-style workflows. The family includes base and long-context variants (8K/32K/128K). The repo ships Python APIs, CLI and web demos (Gradio/Streamlit), an OpenAI-format API server, and a compact fine-tuning kit. Quantization (4/8-bit),... -
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LLM Action
Technical principles related to large models
LLM-Action is a knowledge/tutorial/repository that shares principles, techniques, and real-world experience related to large language models (LLMs), focusing on LLM engineering, deployment, optimization, inference, compression, and tooling. It organizes content in domains like training, inference, compression, alignment, evaluation, pipelines, and applications. Sections covering infrastructure, engineering, and deployment. Repository templates, sample code, and resource links. Articles/code... -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
human-eval is a benchmark dataset and evaluation framework created by OpenAI for measuring the ability of language models to generate correct code. It consists of hand-written programming problems with unit tests, designed to assess functional correctness rather than superficial metrics like text similarity. Each task includes a natural language prompt and a function signature, requiring the model to generate an implementation that passes all provided tests. The benchmark has become a...
